Fabián A. Chavez-Ecos, CHANGE Research Working Group, Facultad de Ciencias de la Salud, Carrera de Medicina Humana, Universidad Científica del Sur, Lima; Red de Cardiología y Salud Pública (RCSP), Ica; Perú
Carlos Quispe-Vicuña, Grupo de Investigación en Neurociencias, Metabolismo, Efectividad Clínica y Sanitaria (NEMECS), Universidad Científica del Sur, Lima, Perú
Leonardo J. Uribe-Cavero, Red de Cardiología y Salud Pública (RCSP), Ica; Sociedad Científica de Estudiantes de Medicina de Ica (SOCEMI), Facultad de Medicina Humana, Universidad Nacional San Luis Gonzaga, Ica. Perú
Wagner Ríos-García, CHANGE Research Working Group, Facultad de Ciencias de la Salud, Carrera de Medicina Humana, Universidad Científica del Sur, Lima; Sociedad Científica de Estudiantes de Medicina de Ica (SOCEMI), Facultad de Medicina Humana, Universidad Nacional San Luis Gonzaga, Ica. Perú
Linda A. Pasapera-Chacaliaza, Sociedad Científica de Estudiantes de Medicina de Ica (SOCEMI), Facultad de Medicina Humana, Universidad Nacional San Luis Gonzaga, Ica, Perú
Luis A. Javier-Contreras, Sociedad Científica de Estudiantes de Medicina de Ica (SOCEMI), Facultad de Medicina Humana, Universidad Nacional San Luis Gonzaga, Ica, Perú
Miguel A. Chavez-Gutarra, Red de Cardiología y Salud Pública (RCSP), Ica, Perú
Kiara Camacho-Caballero, CHANGE Research Working Group, Facultad de Ciencias de la Salud, Carrera de Medicina Humana, Universidad Científica del Sur, Lima, Perú
Objective: Artificial intelligence (IA) is transforming healthcare by enhancing diagnosis and treatment, with up to 90% accuracy in cardiology. However, its adoption faces challenges, including limited training and resources in some regions. While scientific output in Latin America and Caribe (LAC) has grown, it remains low compared to other regions, underscoring the need for innovative solutions to the cardiovascular health crisis. This study analyzes AI-related cardiology research in LAC from 2018 to 2023. Method: This descriptive scientometric study used the Scopus database to analyze AI in cardiology research in LAC. SciVal, VOSviewer, and R Studio were applied to assess publication volume, collaboration types, citations, and research networks. Results: A total of 152 documents, 1,054 citations, and 1,095 authors were identified, averaging 6.9 citations per document. Key topics included atrial fibrillation, percutaneous coronary intervention, and cardiac monitoring. Colombia, Argentina, and Mexico led in scientific output, with international collaboration accounting for 63.8% of publications and an upward trend in research output over time. Conclusions: AI-related cardiology research in LAC is growing, but certain limitations. This analysis highlights key areas and the need to enhance scientific production. It provides a base for future studies and collaborations to address the cardiovascular health crisis and expand AI adoption in cardiovascular care.
Keywords: Artificial intelligence. Cardiology. Bibliometrics.
